stThreat)Geiser C. Challco geiser@alumni.usp.br
cond <- "stThreat"
to_remove <- c('S11')
sub.groups <- c("age","ed.level","intervention","age:intervention",
"ed.level:intervention","age:ed.level:intervention")dat <- read_excel("../data/data-without-outliers.xlsx", sheet = "fss-cond-descriptive")
dat <- dat[!dat$study %in% to_remove, ]
leg <- read_excel("../data/data-without-outliers.xlsx", sheet = "legend")## New names:
## • `` -> `...10`leg <- leg[!leg$study %in% to_remove, ]
idx.e <- which(dat$condition==cond)
idx.c <- which(dat$condition=="control")
data <- data.frame(
study = dat$study[idx.c],
n.e = dat$N[idx.e], mean.e = dat$M.emms[idx.e], sd.e = dat$SD.emms[idx.e],
n.c = dat$N[idx.c], mean.c = dat$M.emms[idx.c], sd.c = dat$SD.emms[idx.c]
)
for (cgroups in strsplit(sub.groups,":")) {
data[[paste0(cgroups, collapse = ":")]] <- sapply(data$study, FUN = function(x) {
paste0(sapply(cgroups, FUN = function(namecol) leg[[namecol]][which(x == leg$study)]), collapse = ":")
})
}
data[["lbl"]] <- sapply(data$study, FUN = function(x) leg$Note[which(x == leg$study)])m.cont <- metacont(
n.e = n.e, mean.e = mean.e, sd.e = sd.e, n.c = n.c, mean.c = mean.c, sd.c = sd.c,
studlab = lbl, data = data, sm = "SMD", method.smd = "Hedges",
fixed = F, random = T, method.tau = "REML", hakn = T, title = paste("Performance in",cond)
)
summary(m.cont)## Review: Performance in stThreat
##
## SMD 95%-CI %W(random)
## S1 0.4174 [-0.0990; 0.9338] 11.0
## S2 0.1925 [-0.2204; 0.6053] 14.3
## S3 -0.2607 [-0.7823; 0.2610] 10.9
## S4 0.2676 [-0.2677; 0.8028] 10.5
## S5 0.3398 [-0.1285; 0.8082] 12.4
## S6 0.2142 [-0.2581; 0.6865] 12.3
## S7 0.3025 [-0.1003; 0.7053] 14.7
## S10: Only use prompt msgs -0.4296 [-0.8583; -0.0010] 13.8
##
## Number of studies combined: k = 8
## Number of observations: o = 600
##
## SMD 95%-CI t p-value
## Random effects model 0.1274 [-0.1287; 0.3835] 1.18 0.2778
##
## Quantifying heterogeneity:
## tau^2 = 0.0393 [0.0000; 0.3290]; tau = 0.1981 [0.0000; 0.5736]
## I^2 = 40.8% [0.0%; 73.9%]; H = 1.30 [1.00; 1.96]
##
## Test of heterogeneity:
## Q d.f. p-value
## 11.83 7 0.1063
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.cont, digits=2, digits.sd = 2, test.overall = T, label.e = cond)
m.sg4sub <- update.meta(m.cont, subgroup = age, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance in stThreat
##
## SMD 95%-CI %W(random) age
## S1 0.4174 [-0.0990; 0.9338] 11.0 adolescent
## S2 0.1925 [-0.2204; 0.6053] 14.3 adolescent
## S3 -0.2607 [-0.7823; 0.2610] 10.9 adolescent
## S4 0.2676 [-0.2677; 0.8028] 10.5 adult
## S5 0.3398 [-0.1285; 0.8082] 12.4 adult
## S6 0.2142 [-0.2581; 0.6865] 12.3 adult
## S7 0.3025 [-0.1003; 0.7053] 14.7 adult
## S10: Only use prompt msgs -0.4296 [-0.8583; -0.0010] 13.8 adolescent
##
## Number of studies combined: k = 8
## Number of observations: o = 600
##
## SMD 95%-CI t p-value
## Random effects model 0.1274 [-0.1287; 0.3835] 1.18 0.2778
##
## Quantifying heterogeneity:
## tau^2 = 0.0393 [0.0000; 0.3290]; tau = 0.1981 [0.0000; 0.5736]
## I^2 = 40.8% [0.0%; 73.9%]; H = 1.30 [1.00; 1.96]
##
## Test of heterogeneity:
## Q d.f. p-value
## 11.83 7 0.1063
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## age = adolescent 4 -0.0251 [-0.6469; 0.5968] 0.0953 0.3088 8.09 62.9%
## age = adult 4 0.2839 [ 0.1999; 0.3680] 0 0 0.15 0.0%
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 2.46 1 0.1171
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)
m.sg4sub <- update.meta(m.cont, subgroup = ed.level, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance in stThreat
##
## SMD 95%-CI %W(random) ed.level
## S1 0.4174 [-0.0990; 0.9338] 11.0 upper-secundary
## S2 0.1925 [-0.2204; 0.6053] 14.3 upper-secundary
## S3 -0.2607 [-0.7823; 0.2610] 10.9 upper-secundary
## S4 0.2676 [-0.2677; 0.8028] 10.5 higher-education
## S5 0.3398 [-0.1285; 0.8082] 12.4 higher-education
## S6 0.2142 [-0.2581; 0.6865] 12.3 higher-education
## S7 0.3025 [-0.1003; 0.7053] 14.7 unknown
## S10: Only use prompt msgs -0.4296 [-0.8583; -0.0010] 13.8 upper-secundary
##
## Number of studies combined: k = 8
## Number of observations: o = 600
##
## SMD 95%-CI t p-value
## Random effects model 0.1274 [-0.1287; 0.3835] 1.18 0.2778
##
## Quantifying heterogeneity:
## tau^2 = 0.0393 [0.0000; 0.3290]; tau = 0.1981 [0.0000; 0.5736]
## I^2 = 40.8% [0.0%; 73.9%]; H = 1.30 [1.00; 1.96]
##
## Test of heterogeneity:
## Q d.f. p-value
## 11.83 7 0.1063
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## ed.level = upper-secundary 4 -0.0251 [-0.6469; 0.5968] 0.0953 0.3088 8.09 62.9%
## ed.level = higher-education 3 0.2748 [ 0.1118; 0.4377] 0 0 0.14 0.0%
## ed.level = unknown 1 0.3025 [-0.1003; 0.7053] -- -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 2.30 2 0.3160
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)
m.sg4sub <- update.meta(m.cont, subgroup = intervention, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance in stThreat
##
## SMD 95%-CI %W(random)
## S1 0.4174 [-0.0990; 0.9338] 11.0
## S2 0.1925 [-0.2204; 0.6053] 14.3
## S3 -0.2607 [-0.7823; 0.2610] 10.9
## S4 0.2676 [-0.2677; 0.8028] 10.5
## S5 0.3398 [-0.1285; 0.8082] 12.4
## S6 0.2142 [-0.2581; 0.6865] 12.3
## S7 0.3025 [-0.1003; 0.7053] 14.7
## S10: Only use prompt msgs -0.4296 [-0.8583; -0.0010] 13.8
## intervention
## S1 Gender-stereotype color, ranking, badges, and avatar
## S2 Gender-stereotype color, ranking, badges, and avatar
## S3 Gender-stereotype color, ranking, badges, and avatar
## S4 Gender-stereotype color, ranking, badges, and avatar
## S5 Gender-stereotype color, ranking, badges, and avatar
## S6 Gender-stereotype color, ranking, badges, and avatar
## S7 Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 8
## Number of observations: o = 600
##
## SMD 95%-CI t p-value
## Random effects model 0.1274 [-0.1287; 0.3835] 1.18 0.2778
##
## Quantifying heterogeneity:
## tau^2 = 0.0393 [0.0000; 0.3290]; tau = 0.1981 [0.0000; 0.5736]
## I^2 = 40.8% [0.0%; 73.9%]; H = 1.30 [1.00; 1.96]
##
## Test of heterogeneity:
## Q d.f. p-value
## 11.83 7 0.1063
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q I^2
## intervention = Gender-stereotype color, rankin ... 7 0.2202 [ 0.0337; 0.4067] 0 0 4.28 0.0%
## intervention = Gender-stereotyped motivational ... 1 -0.4296 [-0.8583; -0.0010] -- -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 7.87 1 0.0050
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)
m.sg4sub <- update.meta(m.cont, subgroup = `age:intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance in stThreat
##
## SMD 95%-CI %W(random)
## S1 0.4174 [-0.0990; 0.9338] 11.0
## S2 0.1925 [-0.2204; 0.6053] 14.3
## S3 -0.2607 [-0.7823; 0.2610] 10.9
## S4 0.2676 [-0.2677; 0.8028] 10.5
## S5 0.3398 [-0.1285; 0.8082] 12.4
## S6 0.2142 [-0.2581; 0.6865] 12.3
## S7 0.3025 [-0.1003; 0.7053] 14.7
## S10: Only use prompt msgs -0.4296 [-0.8583; -0.0010] 13.8
## age:intervention
## S1 adolescent:Gender-stereotype color, ranking, badges, and avatar
## S2 adolescent:Gender-stereotype color, ranking, badges, and avatar
## S3 adolescent:Gender-stereotype color, ranking, badges, and avatar
## S4 adult:Gender-stereotype color, ranking, badges, and avatar
## S5 adult:Gender-stereotype color, ranking, badges, and avatar
## S6 adult:Gender-stereotype color, ranking, badges, and avatar
## S7 adult:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs adolescent:Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 8
## Number of observations: o = 600
##
## SMD 95%-CI t p-value
## Random effects model 0.1274 [-0.1287; 0.3835] 1.18 0.2778
##
## Quantifying heterogeneity:
## tau^2 = 0.0393 [0.0000; 0.3290]; tau = 0.1981 [0.0000; 0.5736]
## I^2 = 40.8% [0.0%; 73.9%]; H = 1.30 [1.00; 1.96]
##
## Test of heterogeneity:
## Q d.f. p-value
## 11.83 7 0.1063
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau Q
## age:intervention = adolescent:Gender-stereotype co ... 3 0.1247 [-0.6949; 0.9444] 0.0403 0.2008 3.43
## age:intervention = adult:Gender-stereotype color, ... 4 0.2839 [ 0.1999; 0.3680] 0 0 0.15
## age:intervention = adolescent:Gender-stereotyped m ... 1 -0.4296 [-0.8583; -0.0010] -- -- 0.00
## I^2
## age:intervention = adolescent:Gender-stereotype co ... 41.7%
## age:intervention = adult:Gender-stereotype color, ... 0.0%
## age:intervention = adolescent:Gender-stereotyped m ... --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 11.09 2 0.0039
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)
m.sg4sub <- update.meta(m.cont, subgroup = `ed.level:intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance in stThreat
##
## SMD 95%-CI %W(random)
## S1 0.4174 [-0.0990; 0.9338] 11.0
## S2 0.1925 [-0.2204; 0.6053] 14.3
## S3 -0.2607 [-0.7823; 0.2610] 10.9
## S4 0.2676 [-0.2677; 0.8028] 10.5
## S5 0.3398 [-0.1285; 0.8082] 12.4
## S6 0.2142 [-0.2581; 0.6865] 12.3
## S7 0.3025 [-0.1003; 0.7053] 14.7
## S10: Only use prompt msgs -0.4296 [-0.8583; -0.0010] 13.8
## ed.level:intervention
## S1 upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S2 upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S3 upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S4 higher-education:Gender-stereotype color, ranking, badges, and avatar
## S5 higher-education:Gender-stereotype color, ranking, badges, and avatar
## S6 higher-education:Gender-stereotype color, ranking, badges, and avatar
## S7 unknown:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs upper-secundary:Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 8
## Number of observations: o = 600
##
## SMD 95%-CI t p-value
## Random effects model 0.1274 [-0.1287; 0.3835] 1.18 0.2778
##
## Quantifying heterogeneity:
## tau^2 = 0.0393 [0.0000; 0.3290]; tau = 0.1981 [0.0000; 0.5736]
## I^2 = 40.8% [0.0%; 73.9%]; H = 1.30 [1.00; 1.96]
##
## Test of heterogeneity:
## Q d.f. p-value
## 11.83 7 0.1063
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2 tau
## ed.level:intervention = upper-secundary:Gender-stereoty ... 3 0.1247 [-0.6949; 0.9444] 0.0403 0.2008
## ed.level:intervention = higher-education:Gender-stereot ... 3 0.2748 [ 0.1118; 0.4377] 0 0
## ed.level:intervention = unknown:Gender-stereotype color ... 1 0.3025 [-0.1003; 0.7053] -- --
## ed.level:intervention = upper-secundary:Gender-stereoty ... 1 -0.4296 [-0.8583; -0.0010] -- --
## Q I^2
## ed.level:intervention = upper-secundary:Gender-stereoty ... 3.43 41.7%
## ed.level:intervention = higher-education:Gender-stereot ... 0.14 0.0%
## ed.level:intervention = unknown:Gender-stereotype color ... 0.00 --
## ed.level:intervention = upper-secundary:Gender-stereoty ... 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 10.58 3 0.0142
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)
m.sg4sub <- update.meta(m.cont, subgroup = `age:ed.level:intervention`, random = T, fixed = F)
summary(m.sg4sub)## Review: Performance in stThreat
##
## SMD 95%-CI %W(random)
## S1 0.4174 [-0.0990; 0.9338] 11.0
## S2 0.1925 [-0.2204; 0.6053] 14.3
## S3 -0.2607 [-0.7823; 0.2610] 10.9
## S4 0.2676 [-0.2677; 0.8028] 10.5
## S5 0.3398 [-0.1285; 0.8082] 12.4
## S6 0.2142 [-0.2581; 0.6865] 12.3
## S7 0.3025 [-0.1003; 0.7053] 14.7
## S10: Only use prompt msgs -0.4296 [-0.8583; -0.0010] 13.8
## age:ed.level:intervention
## S1 adolescent:upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S2 adolescent:upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S3 adolescent:upper-secundary:Gender-stereotype color, ranking, badges, and avatar
## S4 adult:higher-education:Gender-stereotype color, ranking, badges, and avatar
## S5 adult:higher-education:Gender-stereotype color, ranking, badges, and avatar
## S6 adult:higher-education:Gender-stereotype color, ranking, badges, and avatar
## S7 adult:unknown:Gender-stereotype color, ranking, badges, and avatar
## S10: Only use prompt msgs adolescent:upper-secundary:Gender-stereotyped motivational message prompts
##
## Number of studies combined: k = 8
## Number of observations: o = 600
##
## SMD 95%-CI t p-value
## Random effects model 0.1274 [-0.1287; 0.3835] 1.18 0.2778
##
## Quantifying heterogeneity:
## tau^2 = 0.0393 [0.0000; 0.3290]; tau = 0.1981 [0.0000; 0.5736]
## I^2 = 40.8% [0.0%; 73.9%]; H = 1.30 [1.00; 1.96]
##
## Test of heterogeneity:
## Q d.f. p-value
## 11.83 7 0.1063
##
## Results for subgroups (random effects model):
## k SMD 95%-CI tau^2
## age:ed.level:intervention = adolescent:upper-secundary:Gend ... 3 0.1247 [-0.6949; 0.9444] 0.0403
## age:ed.level:intervention = adult:higher-education:Gender-s ... 3 0.2748 [ 0.1118; 0.4377] 0
## age:ed.level:intervention = adult:unknown:Gender-stereotype ... 1 0.3025 [-0.1003; 0.7053] --
## age:ed.level:intervention = adolescent:upper-secundary:Gend ... 1 -0.4296 [-0.8583; -0.0010] --
## tau Q I^2
## age:ed.level:intervention = adolescent:upper-secundary:Gend ... 0.2008 3.43 41.7%
## age:ed.level:intervention = adult:higher-education:Gender-s ... 0 0.14 0.0%
## age:ed.level:intervention = adult:unknown:Gender-stereotype ... -- 0.00 --
## age:ed.level:intervention = adolescent:upper-secundary:Gend ... -- 0.00 --
##
## Test for subgroup differences (random effects model):
## Q d.f. p-value
## Between groups 10.58 3 0.0142
##
## Details on meta-analytical method:
## - Inverse variance method
## - Restricted maximum-likelihood estimator for tau^2
## - Q-profile method for confidence interval of tau^2 and tau
## - Hartung-Knapp adjustment for random effects model
## - Hedges' g (bias corrected standardised mean difference; using exact formulae)forest(m.sg4sub, digits=2, digits.sd = 2, test.overall = T, label.e = cond)
m.cont <- update.meta(m.cont, studlab = data$study)
summary(eggers.test(x = m.cont))## Eggers' test of the intercept
## =============================
##
## intercept 95% CI t p
## 0.883 -8.52 - 10.28 0.184 0.86
##
## Eggers' test does not indicate the presence of funnel plot asymmetry.funnel(m.cont, xlab = "Hedges' g", studlab = T, legend=T, addtau2 = T)